Fetal data processing system and method employing a time-frequency representation

ABSTRACT

A fetal data processing system and method and a fetal monitor and method for monitoring the condition of a fetus are disclosed. A fetal heart rate time series is received and sampled. A non-linear time-frequency transformation is performed to generate a time-frequency representation of the fetal heart rate time series for heart rate time series data spanning a time period which is preferably less than ten seconds. Analysis of fetal heart rate and fetal heart rate variability and other available data is performed to evaluate fetal well-being. Because of the high time resolution of the transformation, short-term transient variations in heart rate and heart rate variability are considered in the analysis.

RELATED APPLICATIONS

[0001] This application is a continuation of U.S. National Phaseapplication having application Ser. No. 08/809,401 filed on May 29,1997, which corresponds to International Application No. PCT/US95/12014filed Sep. 21, 1995, which is a continuation-in-part of U.S. patentapplication Ser. No. 08/309,856 filed Sep. 21, 1994 which issued as U.S.Pat. No. 5,596,993 on Jan. 28, 1997, the entire teachings of theseapplications being incorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] Fetal monitors of various types are widely used in the obstetricsfield. Most of the devices provide to the clinician an indication offetal heart rate (HR) as one item of data used by the clinician toevaluate overall fetal well-being. Other more sophisticated devicesperform frequency analysis on heart rate time data to produceindications of heart rate variability (HRV), another very important itemof data used to monitor fetal condition.

[0003] Typically, these frequency analysis devices collect fetal heartrate time data over relatively long periods of time. At the end of eachperiod, a linear time-frequency representation, such as a short-termFourier transform or a fast Fourier transform, is computed on the timedata to obtain a frequency distribution for the data. In one such priorsystem, each time data window is 30-60 seconds in duration.

[0004] A significant drawback to these devices is that, because of thelong time data window duration, or, equivalently, the low timeresolution of the device, short term or transient variations in heartrate and/or heart rate variability cannot be detected. These transientchanges can be indicators of significant fetal characteristics such asfetal breathing and should be considered in evaluating overall fetalwell-being. Because these prior systems have long time windows,transient changes are not detected, and, therefore the often criticallyimportant fetal characteristics associated with the transient changesare not taken into consideration in the overall fetal evaluation.

SUMMARY OF THE INVENTION

[0005] The present invention is directed to a fetal data processingsystem and method and a fetal monitor and method for monitoring fetalcondition which overcomes the drawbacks of prior art monitors.Specifically, the invention analyzes fetal heart rate data in both timeand frequency domains with sufficient time resolution to detecttransient changes in fetal heart rate and fetal heart rate variabilitywhile also detecting other important heart rate and heart ratevariability characteristics. The invention also has sufficient frequencyresolution to provide a substantial improvement in accuracy of frequencyanalysis and in diagnostic capabilities over prior systems. This isparticularly true during the human birthing process and/or when themother or fetus are at risk due to medication, disease, injury, or otherreason.

[0006] The system or monitor of the invention receives fetal heart ratetime data and samples the data at preferably periodic intervals. Theinvention transforms the time data into the frequency domain bycomputing a time-frequency representation (TFR), in one embodiment, anon-linear TFR, for the time data. In one embodiment, a non-linear TFRis computed for time data covering a time interval of no more than tenseconds; the time interval is preferably in the range between 0.1 and1.0 second. The system then analyzes the non-linear TFR to indicate acondition of the fetus.

[0007] In one embodiment, the time data is sampled at 0.25 secondintervals. For each sample in this embodiment, the invention computes anon-linear time-frequency representation (TFR) of the data. It thenanalyzes the non-linear TFR to indicate the condition of the fetus andto indicate fetal well-being. In other embodiments, the data is sampledat the same 0.25 second interval, but the TFR may not be calculated onevery data sample. Instead, it may be calculated at greater intervals.Preferably, the interval between recalculation of the TFR will notexceed ten seconds.

[0008] As noted above, the sampling interval is preferably 0.25 secondor, equivalently, the sampling frequency is preferably 4 Hz. Othersampling frequencies can be used. However, the frequency will preferablynever be below 2 Hz to meet the Nyquist criterion for certain signalfrequencies in heart rate data near 1 Hz, as will be described below indetail. Lower sampling frequencies can be used if anti-aliasing filtersare used.

[0009] In the system of the invention, the fetal heart rate time seriesis obtained from either a separate internal or external fetal heartmonitor. The time series is first high-pass filtered to remove the DCand very low frequency components. Next, the signal is made analytic bya Hilbert Transform, and the non-linear time-frequency representation(TFR) is then obtained for the signal. In one preferred embodiment, thenon-linear TFR is computed by a quadratic transformation process such asa smoothed Wigner distribution.

[0010] The TFR is essentially an amplitude-versus-frequency plot of thefrequency content of the heart rate time series which is updated overtime. There are a number of methods used to calculate TFRs. Theseinclude linear techniques such as the short-term Fourier transform, thewavelet transform and non-linear transformations belonging to the Cohenclass of TFRs. The non-linear transformation results in substantialimprovement in both temporal and frequency resolutions compared to theshort-term Fourier transform. In the preferred embodiment, the smoothedWigner transform is used, although other techniques from Cohen's classof TFRs, such as the Choi-Williams or Cone Kernel distributions couldalso be used. The wavelet transform, although a linear technique, hasbetter time resolution, particularly at higher frequencies, than theshort-term Fourier transform. The wavelet transform is an alternativemethod of calculating the TFR. Improved resolution in both the time andfrequency domains allows for changes in the frequency content of theheart rate time signal to be quantified and located to a specific periodof time. Thus, the system and monitor of the invention provide highlydesirable frequency and temporal resolution to allow more accurateevaluation of overall fetal well-being.

[0011] In a preferred embodiment, for each new sample of heart rate, thenon-linear TFR is calculated. Then, the area under theamplitude-frequency plot is calculated over specified frequency bands.These areas are then presented for display to the clinician and are alsoused by the monitor to assess fetal condition. As an example of themultiple frequency bands, sample areas are defined as follows:

[0012] A_(HF): High frequency (HF) 0.50-1.1 Hz

[0013] A_(MF): Mid frequency (MF) 0.15-0.50 Hz

[0014] A_(LF): Low frequency (LF) 0.02-0.50 Hz

[0015] The MF and HF bands are modulated solely by the parasympatheticnervous system since sympathetic modulation of heart rate is minimalabove 0.15 Hz. The LF band should reflect both sympathetic andparasympathetic modulation of heart rate variability.

[0016] It will be understood that the frequency bands listed above, aswell as the total frequency range, are used for illustration purposesonly and not as limitations. Other frequency bands and ranges can beused. The definition of the frequency bands are based on currentunderstanding of the physiology. These are envisioned as the defaultdefinitions. The user is able to enter new values for the frequenciesdefining the bands or even change the number of bands analyzed. Inaddition, the user has the capability of defining ratios and/or sums ofpairs of areas.

[0017] In addition to monitoring the heart rate time series and heartrate variability, the invention can also monitor optional input signals.These include a uterine contraction signal and a fetal movement signal.By analyzing various combinations of the available input data, theinvention can indicate several fetal characteristics. These includechanges in fetal state, fetal breathing movements, fetal body movements,fetal heart rate accelerations and decelerations, fetal heart ratevariability and transient changes in fetal heart rate and fetal heartrate variability.

[0018] The system of the invention includes numerous subsystems forprocessing and analyzing input data and assessing fetal condition. Atime-frequency representation (TFR) subsystem performs thetransformation of the heart rate time data into the frequency domain. Itreceives the heart rate time series data, high-pass filters the data,and performs the Hilbert Transform to make the heart rate signalanalytic. Then, it calculates the non-linear TFR on the analytic heartrate signal, preferably by a smoothed Wigner distribution. As notedabove, other specific implementations use other known non-lineartransformations such as the Choi-Williams distribution. An areacalculation module of the TFR subsystem computes the areas under theTFR.

[0019] A feature extraction subsystem receives as inputs the heart ratetime series, the TFR, the area calculations, the uterine contractionsignal and the fetal movement signal and analyzes the data to compilethe data into a feature vector. The feature vector is a collection ofdata which includes values for all of the variables used to perform thefetal assessment process. These variables are periodically updated asthe time series is sampled. That is, in one embodiment, a new featurevector is generated for every sample of the heart rate time series.

[0020] The feature vector and various user inputs are received by anexpert subsystem of the invention. The principal function of the expertsubsystem is to classify data contained in the feature vector and theuser inputs to make an assessment of fetal condition and well-being. Itincludes multiple first-stage classifiers which receive the featurevector and make preliminary assessments as to fetal state, heart ratedeceleration and acceleration patterns and uterine contraction pattern.In one embodiment, the initial or first-stage classifiers arerule-based. In other embodiments, one or more of them are neuralnetworks. These assessments as well as certain user inputs such asgestational age are forwarded to a rule-based front end module, thepurpose of which is to determine, based on the inputs from thefirst-stage classifiers and the user inputs, which of multipleclassifiers in an outcome predictor module will analyze the fetalpattern. The rule-based front end selects a classifier in the outcomepredictor and forwards the appropriate data to the selected classifier.The final classification of the data patterns is accomplished by one ofthe neural network classifiers in the outcome predictor module.

[0021] The outcome predictor module includes the multiple neural networkclassifiers which receive data patterns from the rule-based front end.Based on the input pattern, the appropriate classifier outputs a signalindicative of fetal condition or well-being. The signal classifies thefetal condition as being either normal, stressed, indeterminate orominous and includes an associated probability. Other conditionclassifiers can also be used to indicate specific fetal states ofinterest. The output is received by a rule-based back end module of theexpert subsystem. The module also receives the data selected by therule-based front end for display to the clinician. The rule-based backend receives the data, the classification from the outcome predictor,the outputs of the first-stage classifiers and any user inputs andformats all of this data for presentation to the clinician on the systemdisplay.

[0022] Since the clinician is not likely to accept the classification bythe expert subsystem without review of the data relied upon in makingthe classification, the data is available to the clinician on the systemdisplay. The display and storage subsystem organizes the data fordisplay on the monitor, for printing on a strip chart and/or for storagein a data file.

[0023] In one embodiment, the screen display is divided into threewindows. One of the windows acts like a strip chart to display importantdata as a function of time. A second window is a “smart” window in thatwhat is displayed in the window is determined by the output of theclassifiers in the expert subsystem. Based on the classification made bythe expert subsystem, certain variables are automatically selected fordisplay in the smart window with no intervention from the clinician,enabling the clinician to make an independent assessment of fetalcondition using the displayed data. The third window is a text windowwhich conveys information from the output of the classifiers, somesummary data, the prediction of fetal well-being from the outcomepredictor, and recommendations or warnings.

[0024] A number of predefined display configurations for all of thedisplay windows are available. In addition, the user can define his/herown configurations. In another embodiment, the display capability of themonitor includes a real-time strip chart recorder. Thus, a hard copyrecord of pertinent data is available to the clinician for review.

[0025] In an alternative preferred embodiment of the invention, thedisplay can present a three-dimensional plot of the TFR. Thethree-dimensional display preferably provides a visual interpretation ofthe fetal heart rate data as it is collected and processed. In oneparticular embodiment, the three-dimensional plot is updated withfrequency-amplitude data at 1.0-second intervals. The visual displayprovides the clinician with the ability to quickly identify trends aswell as transients in heart rate and heart rate variability tofacilitate the conclusion as to fetal well-being.

[0026] Other signals such as the contraction signal and/or the fetalmovement signal can be displayed with the three-dimensional TFR display.Alternatively, the three-dimensional TFR plot can be color coded toindicate conditions of the contraction and/or fetal movement signals.For example, during a contraction, the TFR plot can be produced in red,and, between contractions, it can be blue. This display format providesthe clinician with a global view of how fetal heart rate variability ischanging over time. In one preferred embodiment of the invention, theuser has the option of reviewing the raw TFR data at any time or cansubstitute the three-dimensional TFR display for one of the threewindows described above. Alternatively, the three-dimensional TFRdisplay can replace all of the windows on the display.

[0027] Many realizations of the invention are possible. One suchrealization involves implementing the assessment process on a personalcomputer. In this embodiment, inputs to the computer are obtained fromthe outputs of various commercially available prior art monitors. Ananalog-to-digital board in the computer samples the heart rate timesignal and the contraction and fetal movement signals if available.Calculations and analysis are performed on the computer by the varioussubsystems which can include a digital signal processing board and/or aneural network board and which are controlled by software running on aprocessor and stored in memory in the computer.

[0028] In another embodiment, the invention is a stand-alone device. Theinvention is implemented in hardware and/or software using a processorand memory inside the monitor.

[0029] The system of the invention provides numerous advantages overother devices. For example, as previously discussed, the use of anon-linear time-frequency representation allows for changes in heartrate variability to be located to specific periods of time. Thisimprovement in time resolution allows the device to detect criticaltransient episodic fetal characteristics. This facilitates a much moreinformed decision as to overall fetal well-being.

[0030] The invention also combines the benefits of both rule-basedclassification and neural network classification. Initialclassifications of data patterns are performed by rule-based classifiersto identify data pertinent to a particular fetal condition. Theseinitial classifications allow the data pertinent to an indicatedphysical characteristic to be separated from the remaining fetal data.

[0031] The final assessment as to fetal well-being is performed by aneural network to eliminate certain drawbacks of rule-based finaldecision making. It has been shown through experiments that two expertscan interpret the same fetal data as indicating different fetalconditions. In addition, it has also been shown that the same cliniciancan interpret the same data differently on different occasions. Sincerules in a rule-based system must be compiled from experts, generating alibrary of rules for the rule-based system can be an inaccurate processgiven the disparity among expert opinions. In the present invention, theneural networks are “trained” on a database in which fetal well-beinghas been determined by objective criteria, e.g., by Apgar score, bloodgas measurements and/or neural development. Thus, inaccuracies due tothe nature of the process for extracting rules from experts for such acomplex decision making task are eliminated.

[0032] It is also recognized that in a neural network based system, aclinician will not readily accept a final decision since therelationships among patterns developed by the neural network duringtraining cannot be readily discerned from the network. Therefore, thebasis for the classification of certain test data is unknown. Thepresent invention presents on the display the data used by the neuralnetwork in its classification to allow the clinician to arrive athis/her own assessment without being required to rely on theclassification made by the system of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescription of preferred embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention.

[0034] FIGS. 1A-1H contain time plots of variables showing a change infetal state.

[0035] FIGS. 2A-2H contain time plots of variables showing fetalbreathing efforts.

[0036] FIGS. 3A-3H contain time plots of variables showing fetal bodymovements.

[0037] FIGS. 4A-4H contains time plots of variables used to illustrateanalysis of transient change in heart rate variability independent ofchanges in heart rate in the context of active labor during pushing.

[0038] FIGS. 5A-5B contains time plots of heart rate and the contractionsignal showing a loss of variability during a deceleration.

[0039]FIG. 6 contains time plots of heart rate and the contractionsignal showing a period of increased heart rate variability.

[0040] FIGS. 7A-7B contain time plots of heart rate and the contractionsignal showing a sinusoidal variation in heart rate.

[0041]FIG. 8 is a top-level block diagram of the fetal data processingsystem of the invention.

[0042]FIG. 9 is a block diagram of the time-frequency representationsubsystem of the invention.

[0043]FIG. 10 is a block diagram of the feature extraction subsystem ofthe invention.

[0044]FIG. 11 contains schematic time plots of the heart rate signal andcontraction signal illustrating the definition of certain variables usedby the system of the invention.

[0045]FIG. 12 is a block diagram of the expert subsystem of theinvention.

[0046]FIG. 13 is a schematic illustration of one configuration of thedisplay of the invention.

[0047]FIG. 14 is a schematic block diagram of one realization of thesystem of the invention.

[0048]FIG. 15 is a schematic block diagram of an alternative realizationof the system of the invention.

[0049]FIG. 16 is a schematic block diagram of the fetal monitor used inthe realization of the invention shown in FIG. 15.

[0050]FIG. 17 is a schematic illustration of a three-dimensional plot ofthe time-frequency representation of the invention which can be producedon the display of the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0051] In interpreting a fetal heart rate time trace, a clinician mustmake assessments concerning the baseline heart rate (HR) as well as thebaseline variability of heart rate. In addition, clinicians must be ableto classify transient changes in heart rate and heart rate variability(HRV) as to type and magnitude. These indices are important to thediagnosis of fetal distress. Optimal management of the stressed fetusalso requires assessment of the progress over time of the fetal HRpattern as well as labor. Although determination of baseline HR isrelatively straightforward from a standard fetal trace, quantitativeassessment of HRV is more difficult. The present invention provides theclinician with a detailed, quantitative and continuous assessment ofHRV. In addition, features important in assessing fetal well-being arecontinuously extracted and available for display to the clinician or asinput to an expert subsystem in which an automatic assessment of fetalwell-being is performed.

[0052] HR and HRV are controlled by the autonomic nervous system. Theautonomic nervous system has two important branches; the sympathetic andparasympathetic nervous systems. Activation of the sympathetic nervoussystem elicits an increase in HR. Activation of the parasympatheticnervous system results in a decrease in HR. Since the influence of theparasympathetic nervous system on the heart is mediated exclusively bythe vagus nerves, these effects are also referred to as vagalmechanisms. The effects of alter autonomic tone on HRV is complex andmuch information has been obtained in the adult by use of spectralanalysis.

[0053] It has been determined that if one examines HRV in the frequencydomain as a spectral plot of amplitude versus frequency, variation inHRV at frequencies above 0.15 Hz are due to parasympathetic, i.e.,vagal, mechanisms. Typically, there is a peak in the amplitude spectrumat about 0.25 Hz, the typical frequency of breathing, corresponding torespiratory modulated changes in HR. These changes in HR are mediatedexclusively by parasympathetic mechanisms. Typically in the adult, twoor more peaks can be detected in the amplitude spectrum at frequenciesbelow 0.25 Hz. These are mediated by both sympathetic andparasympathetic mechanisms. Changes in the control of the heart by theautonomic nervous system are reflected by changes in the frequencydomain description of HRV. For example, patients with congestive heartfailure have a decreased amplitude spectrum. In addition, an orthostaticstress such as tilt will cause an activation of arterial baroreceptorswhich will increase sympathetic and decrease parasympathetic activities.The amplitude spectrum will exhibit a decrease in high frequencyactivity, but an increase in low frequency consistent with the changesin autonomic nervous system activity.

[0054] The present invention applies frequency domain techniques tofetal assessment. However, the techniques commonly used in the adultcannot be applied to the analysis of fetal HRV since these methodsrequire that the signal be stationary, i.e., that the characteristics ofthe patient do not change over the period of analysis. Thecharacteristics of the fetus are changing, often over very short periodsof time. These changes can be due to alterations in sleep state,periodic stress induced by uterine contractions, and fetal body andbreathing movements, both of which are highly variable and episodic innature. In addition, there is information crucial to the assessment offetal well-being in these transients and nonstationarities.

[0055] As will be described below in detail, the system of the inventionreceives a heart rate time series obtained from another monitor or froman internal monitor. The signal is first high-pass filtered, removingthe DC and very low frequency components. The signal is then madeanalytic by a Hilbert Transform, and a non-linear time-frequencyrepresentation (TFR) of the signal is calculated on a point-by-pointbasis.

[0056] In order to present the TFR to the clinician in a concise andmeaningful way, the area under the amplitude-frequency plot iscalculated over specified frequency bands. These areas are then outputfor display. As an example, the frequency bands can be defined asfollows:

[0057] A_(HF): High Frequency (HF) 0.50-1.1 Hz

[0058] A_(MF): Mid frequency (MF) 0.15-0.50 Hz

[0059] A_(LF): Low frequency (LF) 0.02-0.50 Hz

[0060] The MF and HF band are modulated solely by the parasympatheticnervous system since sympathetic modulation of HR is minimal above 0.15Hz. When episodic fetal breathing efforts occur, the frequency ofbreathing is typically 0.5 to 1.0 Hz. Thus, any vagally mediatedmodulation of HRV by breathing efforts is demonstrated in the highfrequency band. The LF band should reflect both sympathetic andparasympathetic modulation of HRV, and includes contributions frombaroreceptors, fetal body movements, and temperature regulation systems.

[0061] Total area A_(T) under the amplitude-frequency curve from 0.02 to1.1 Hz is a measure of overall HRV. The sum A_(MH)=A_(MF)+A_(HF)provides a measure, although incomplete, of vagal modulation. The ratioAR=A_(MH)/A_(LF) provides an index of overall sympatho-vagal balance.Also defined is the ratio A_(B)=A_(HF)/A_(MF). The variables A_(LF),A_(MF), A_(HF), A_(T), A_(MH), A_(B) and A_(R) are available forcontinuous display on the display of the monitor. These area variablesare referred to collectively herein as A_(X), where X ε {LF, MF, HF, T,MH, B and R}.

[0062] It will be understood that the frequency bands listed above, aswell as the total frequency range, are used for illustration purposesonly and not as limitations. Other frequency bands and ranges can beused. The definition of the frequency bands are based on currentunderstanding of the physiology. These are envisioned as the defaultdefinitions. The user is able to enter new values for the frequenciesdefining the bands or even change the number of bands analyzed. Inaddition, the user has the capability of defining ratios and/or sums ofpairs of areas.

[0063] Analysis of the computed areas, the HR trace and HRV indicatesfetal characteristics which in turn indicate overall fetal well-being.That is, certain specific data patterns identify specific associatedfetal characteristics or activities. These characteristics or activitiesinclude change of state, breathing efforts, fetal body movements,transient changes in HRV, HR deceleration and acceleration, andsinusoidal variations in HRV. This list is not intended to be exhaustiveas numerous other fetal characteristics can be identified by analyzingdata. To demonstrate the assessment process of the invention, someexamples of the application of the TFR to fetal HRV will now bedescribed.

[0064] +E,uns Changes in state. It has been shown by experiment anddescribed in the literature that after approximately 36 weeks, mostfetuses show evidence of organized behavioral states. Two stages ofsleep as well as wakefulness can be discerned in instrumented lambfetuses. In the human fetus, there are four states which can beidentified by a HRV pattern, the two most common of which are 1F and 2F.State 1F is a low variability state, and 2F is a high variability state.A change between state 1F and 2F can be discerned from HRV, and in orderto distinguish a change in state from other short-term transients, achange in HRV must be sustained for a number of minutes; typically, 3-5minutes have been used in the literature.

[0065] Shifts between states 1F and 2F can be discerned from TFRanalysis by monitoring AT as a function of time. An example is shown inFIGS. 1A-1H. A low variability state may indicate either fetal distressor simply state 1F. Therefore, it is important to have the capability ofdetermining the duration of states. A normal fetus will cycle betweenstates typically at intervals of approximately 30 minutes. As theduration of a low variability state exceeds 30 minutes, the likelihoodthat the pattern indicates a distressed fetus increases.

[0066] +E,uns Detection of Fetal Breathing Movements. Breathingmovements in the fetus are episodic, i.e., they occur in bursts, eachlasting from tens of seconds to a couple of minutes. The frequency ofoccurrence of fetal breathing episodes varies with state, gestationalage, labor, and fetal distress. Therefore, the ability to obtain anindex which could provide information as to the occurrence of fetalbreathing efforts aid the clinician in evaluating fetal well-being.

[0067] During an episode of breathing movements, the frequency ofbreathing typically ranges from 30 to 60 per minute (0.5 to 1.0 Hz).Therefore, the direct effects of breathing efforts on HRV would beexpected to be manifested in the HF range of the TFR. An example of achange in HRV consistent with an episode of fetal breathing is shown inFIG. 2. At approximately minute 13 there is an increase in A_(HF),lasting approximately 30 seconds. There is also an increase in the A_(R)ratio indicating a preferential increase in HF vagal modulationconsistent with an episode of fetal breathing. Close examination of theHR trace reveals the presence of a high frequency variation. However,the presence of this high frequency transient is more apparent from theA_(HF) trace compared to the original HR trace.

[0068] +E,uns Effects of Fetal Body Movements. After 28 weeks gestation,fetal body movements are typically accompanied by changes in HR.Increases in HR with body movements usually indicate a reactive fetusand is considered a sign of fetal well-being. It has been shown that theexact morphology of the change in HR which accompanies fetal bodymovements depends on the pattern of fetal movement. Slow trunk rotationsare associated with slow increases in HR. The stronger the bodymovement, the greater the change in HR. Complex body movement patterns,such as arms with trunk rotation, produce a more complex HR pattern,e.g., the acceleration may be interrupted by brief decelerations. Thesechanges in HR would produce consistent changes in the TFR over time.Changes would be expected predominately in A_(LF) for slow changes,whereas for more complex changes in HR associated with more complexpatterns of body movements would be associated with changes in otherfrequency bands. In addition, increased magnitude of changes in HR withbody movements would be reflected by a greater value in the appropriatefrequency band.

[0069] An example of HRV analysis in a fetus during the early stages oflabor is shown in FIGS. 3A-3H. There are episodic changes in HR whichare not associated in a consistent fashion with contractions. Thus,these likely represent the response to fetal body movements. Differencesin the morphology of the HR changes can be discerned from the responseof the TFR parameters. For example, the HR transient at 2 minutes isslow as evidenced by the dominant peak in A_(LF), whereas the transientat 8 minutes is predominately a higher frequency transient as seen bythe dominate peak in A_(MF). Thus, the application of the TFR to thefetal HR allows the monitor not only to identify but to characterize inmore detail the HR transient.

[0070] +E,uns Transients in HRV. There are times when there may be clearchanges in HRV without concomitant changes in HR. These changes mayreflect accelerations or decelerations as usually defined in theliterature or they may reflect other events. FIGS. 4A-4H present theresults of HRV analysis obtained from a patient late in labor whilepushing. Although there are no consistent changes in the level of HRwith each push, there are reproducible changes in the parameters relatedto HRV. There is an increase in HRV in all frequency bands with eachepisode of pushing. This suggests that the fetal autonomic nervoussystem is responding to the increased intrauterine pressure, which inturn suggests that the fetus is reactive. Such reactivity is a positivesign of fetal well-being. The ability to make this assessment may beparticularly important in this fetus since baseline variability,variability between the contractions, is quite low which could indicateeither fetal distress or behavioral state 1F. Other systems for HRmonitoring during labor do not examine transients in HRV independent ofchanges in HR.

[0071] +E,uns Analysis of Decelerations. One of the most commondiagnostic patterns of fetal HR during labor is that of decelerations,i.e., a decrease in HR which is coupled to contractions. First,decelerations are typically classified by shape, either uniform orvariable. Uniform decelerations are characterized by a similar shapehaving gradual onsets and offsets. However, if the shape ofdecelerations varies from one to the next, they are considered variabledecelerations. Based on the relationship between the timing of theoccurrence of the decelerations and contractions, uniform decelerationsare usually further subdivided into early and late decelerations. Earlydecelerations start with the onset of the contraction. The minimum HRoccurs at the peak of the contraction and the decrease in HR frombaseline is modest. It is thought that these decelerations are reflexmediated, indicating a reactive fetus and are, therefore, not ominous.However, it is very important to distinguish early decelerations fromeither late or variable decelerations.

[0072] Late decelerations are delayed in time relative to thecontraction and the minimum HR occurs after the peak of contraction. Ingeneral, late decelerations are thought to be due to hypoxia. If HRV isgood, the hypoxia can often be helped by maternal hyperoxia orrepositioning. Late decelerations with decreased variability areassociated with an increased incidence of fetal acidosis and low Apgarscore.

[0073] The Apgar score results from an examination of the newbornshortly after birth. The physician or nurse evaluates the newborn'sheart rate, respiratory effort, muscle tone, reflex irritability andskin color. A score of either 0, 1, or 2 is given for each of the fivevariables determined by a simple set of well-known criteria. A score of10 indicates a vital newborn whereas a score less than 7 usuallyindicates a depressed infant which may need resuscitation. Typically,the Apgar score is obtained at 1 and 5 minutes after birth.

[0074] Variable decelerations vary in appearance and temporalrelationship to contractions. The first step in classifying variabledecelerations is to make a determination of size. This includes measuresof absolute depth as well as duration. The next step in classificationof variable decelerations involves determining if classic features arepreserved or whether atypical features are superimposed. Because of thenumber of different mechanisms affecting decelerations, variabledecelerations can be associated with the complete spectrum of fetalwell-being, from a healthy to a moribund fetus.

[0075] There are seven atypical features which may be associated withvariable decelerations. The most ominous of these is a loss ofvariability during the deceleration as shown in FIGS. 5A-5B. FIG. 5A isa time plot of the baseline HR signal and FIG. 5B is a time plot of theuterine time plot of the baseline HR signal and the uterine contractionsignal. The baseline HR signal shows decelerations associated withcontractions and loss of heart rate variability during thedecelerations. Because of its increased time resolution, the TFRtechnique of the invention allows quantification of the loss ofvariability and the localization in time of such a loss, i.e., at thenadir of the deceleration or during recovery, etc. This cannot beaccomplished using prior standard spectral techniques.

[0076] The six remaining atypical features will be briefly described.(1) Loss of primary acceleration is the most frequently encounteredatypical feature. Nearly one third of the infants born with thisatypical feature have an Apgar score less than 7 at 1 minute. However,less than 7% of these infants have an Apgar score less than 7 at 5minutes. (2) Loss of secondary acceleration is the third most commonatypical feature and is of slightly more concern than the loss ofprimary acceleration. (3) A slow return to baseline HR occurs in 60% ofatypical variable decelerations and is associated with a 1 minute Apgarscore less than 7 in 47% of fetuses and a 5 minute Apgar score less than7 in 10% of the fetuses. (4) A prolonged secondary acceleration isthought to be due to hypoxia possibly indicating an abnormal umbilicalcord position. (5) A biphasic deceleration is characterized by aninitial deceleration, a return towards baseline, followed by a seconddeceleration. Thus, the HR trace resembles a W. This is the fifth mostcommonly occurring atypical feature. When present, the incidence of anApgar score less than 7 at 1 minute is 48%, while the incidence of anApgar score less than 7 at 5 minutes is 12%. (6) The least commonatypical feature is a continuation of baseline at a lower level, i.e.,there is a deceleration and HR returns to a level below the originalbaseline. While the presence of this feature predicts an Apgar scoreless than 7 at 1 minute in 43% of the fetuses, only 7% will have anApgar score less than 7 at 5 minutes.

[0077] +E,uns Periods of Increased HRV. Increases in HRV are oftenassociated with hypoxia and, in general, represent normal compensatorymechanisms of a reactive fetus. The occurrence of such a stress patterncan serve as an alert to the onset of fetal hypoxia, which should thenbe monitored to insure that it does not progress to a distress pattern.Shown in FIG. 6 is an example of increased HRV in a term fetus. In thisparticular case, the hypoxia is likely due to the increased uterineactivity as evidenced by an increased baseline tone and a lack of aresting phase.

[0078] +E,uns Marked Sinusoidal Pattern. Many cases of pure sinusoidalpattern reflect a benign fetal response secondary to a drug effect.However, sinusoidal patterns may also be due to severe fetal anemia orperinatal asphyxia. For the latter case, a sinusoidal pattern isparticularly ominous and is consistent with severe fetal jeopardy. Anexample of a marked sinusoidal pattern is shown in FIGS. 7A-7B.

[0079] A sinusoidal HR pattern can be detected by examining each TFR fora dominant peak at a frequency of about 1 cycle per 2-5 minutes. One ofthe difficulties in identifying a sinusoidal pattern is to distinguishit from an undulating, but not sinusoidal, pattern. This can be done byexamining the higher harmonics. The value of the TFR at the higherharmonics can be relatively larger for an undulating pattern compared toa true sinusoidal pattern.

[0080] It is important to know how changes in HR and HRV are affected byuterine contractions. Therefore, the time of occurrence and magnitude ofthe peak of uterine contraction, the duration of the contraction, theinterval between successive contractions, and baseline level of thecontraction signal will all be estimated. The source of the contractionsignal (CON) can be an external tocodynamometer or an interuterinepressure transducer.

[0081] Also, many new fetal monitors have the ability to detect fetalmovements. A fetal movement signal (FM) is analyzed for number ofmovements and the time of occurrence of movements. Classifiers includedin the expert subsystem of the monitor will correlate changes invariability with the fetal movements.

[0082] A top-level block diagram of the fetal data processing system 10of the invention is shown in FIG. 8. There are four major subsystems inthe system 10: (1) a time-frequency representation (TFR) subsystem 12for performing the time-frequency transformation to calculate thenon-linear TFR of heart rate, (2) a feature extraction subsystem 14 forextracting and compiling pertinent data features from all of the inputfetal data, (3) an expert subsystem 16, and 4) a display and storagemodule 18. The only required input to the system is the HR time series,generated by either another monitor or software module or by an internalmonitor or software module. Optional inputs to the system 10 includesignals related to the strength of contraction (CON) and fetal movements(FM). The system 10 adjusts the extraction of features as well as theassessment by the expert subsystem 16 as a function of the presence orabsence of these optional inputs.

[0083] Each of the subsystems shown in FIG. 8 will now be described indetail. A block diagram of the TFR subsystem 12 is shown in FIG. 9. Theinput to this subsystem is the HR time series. Initially, the HR timeseries is filtered by a digital highpass filter 20 to remove the DC andvery low frequency components of the signal. The exact form of thedigital high pass filter 20 is not critical, having only the requirementthat the magnitude characteristic be monotonic over the pass band. Thecutoff band is currently set to 0.06 Hz., i.e., frequency componentsbelow this are attenuated. This value was arrived at empirically andmight be altered in subsequent realizations.

[0084] The filtered heart rate signal is then made analytic by a digitalHilbert Transformer 22. Alternatively, the HR time series could be madeanalytic using FFT techniques, but this would introduce delays into theprocessing and is, therefore, not the preferred implementation.

[0085] The non-linear TFR is then calculated on the analytic heart ratesignal. Preferably, this is accomplished using a non-lineartransformation such as a smoothed Wigner distribution 24, although otherspecific implementations of the TFR, such as the Choi-Williamsdistribution, can also be used. A smoothed version of the Wignerdistribution is calculated as:${W\left( {n,m} \right)} = {\frac{1}{2}N{\sum\limits_{k = {{- N} - 1}}^{N - 1}{{{{h(k)}}^{2}\left\lbrack {\sum\limits_{p = {{- M} + 1}}^{M - 1}{{g(p)} \times \left( {n + p + k} \right){x^{*}\left( {n + p - k} \right)}}} \right\rbrack}^{{- 2}\quad \quad \pi \quad {m/N}}}}}$

[0086] where x is the analytic heart rate signal, g(p) is a time domainsmoothing function, h(k) is a symmetric normalized frequency domainwindow function. In the present implementation, g(p) and h(k) arerectangular and Gaussian windows, respectively; although other windowscould be used. The details of the non-linear transformation aredescribed in “Linear and Quadratic Time-Frequency SignalRepresentations,” by F. Hlawatsch et al., IEEE Signal ProcessingMagazine, pp. 21-67, (April, 1992), the contents of which areincorporated herein by reference.

[0087] The input to the area calculation module 26 is the TFR W(n,m).The areas under the amplitude-frequency plot are calculated over thefrequency bands specified above. The outputs of the module 26 are:A_(LF), A_(MF), A_(HF), A_(MH), A_(R), A_(B), and A_(T). These arecollectively referred to herein as A_(X).

[0088] A block diagram of the feature extraction subsystem 14 ispresented in further detail in FIG. 10. The purpose of this subsystem isto calculate and/or identify and extract important variables or featuresfrom the HR time series, the TFR, the calculated areas, the contractionsignal, and the fetal movement signal. The subsystem 14 then gatherspertinent items of data and compiles them into a feature vector.

[0089] The required inputs to the feature extraction subsystem are theHR time series, the TFR of the HR signal (W(n,m)), and the areascalculated from the TFR. Optional inputs include the contraction signaland the fetal movement signal. By making a number of inputs optional,the number of environments in which the monitor can be used isincreased. Thus, in the antepartum environment, having a HR time seriesmay be sufficient for an initial screening of fetal status. However,accurate determination of fetal status during active labor requires asignal indicative of uterine activity. The function of each modulewithin the subsystem 14 will now be described.

[0090] In the statistics module 28, time domain indices of average heartrate and variability are calculated over one-minute blocks. Sinceoccasional transient artifacts are likely in the HR time series, theseindices should be robust in the presence of such artifacts. Thus, themedian heart rate and the inner first quartile range are used. To dothis, all values of HR over the one-minute block are ranked from low tohigh. The median HR, HR_(M), is defined as the HR at which 50% of thevalues are lower and 50% of the values are higher. The inner quartile HRrange, HR_(Q), is defined as the difference between the value of the HRat 12.5% above the median minus the value of the HR at 12.5% below themedian.

[0091] The input to the HR transient detector module 30 is the HR timeseries. This module 30 detects the occurrence of a transient in HR,either an acceleration or a deceleration. The change must be greaterthan a predefined threshold, δ_(HR), and have a minimum duration,τ_(HR). The outputs of the module 30 are an identification of the typeof transient (acceleration or deceleration) and the times of occurrenceof the beginning and end of the transient, T_(TS) and T_(Te),respectively.

[0092] In the sinusoidal pattern detector 32, a sinusoidal heart ratepattern is detected by examining the TFR for a dominate peak. Thedominate peak will be at the fundamental frequency of oscillation. Apurely sinusoidal pattern would have a single peak at the frequency ofoscillation. More complex patterns of oscillation would have significantpeaks at higher harmonics. As an index of how closely the HR patternresembles a pure sinusoid, the ratio of the amplitudes of the TFR at thefundamental and first harmonics will be calculated, i.e., S_(R)=TFR(f₀)/TFR (2f₀), where f₀ is the fundamental frequency of oscillation. AHR pattern will be considered sinusoidal if S_(R) is greater than athreshold δ_(S).

[0093] The contraction analyzer 34 will be invoked if the contractionsignal CON is being analyzed. This module 34 detects the followingparameters from the contraction signal: time of peak contraction T_(Cp);peak magnitude C_(p); time of start of contraction T_(Cs); time of endof contraction T_(Ce); and baseline magnitude C_(B). If the contractionsignal is from a tocodynamometer, the values of the start and end pointsas well as the magnitudes will only be approximate due to inaccuraciesin the sensor methodology. However, if the contraction signal isobtained from an interuterine catheter, these values will be accurate.

[0094] The input to the fetal movement analyzer 36 is the fetal movementsignal. The analyzer 36 detects the occurrences of movements and recorda time for each occurrence.

[0095] The inputs to the acceleration analyzer 38 are: (1) the times forthe start and end of an acceleration, T_(AS) and T_(Ae), respectively,obtained from the HR transient detector module 30, (2) the areascalculated from the TFR curve by the area calculation module 26, i.e.,A_(LF), A_(MF), A_(HF), A_(MH), A_(R), A_(B) and A_(T), and (3) the HRtime series. The peak HR during the acceleration, HRA, and the durationof the transient are calculated from the input data.

[0096] The inputs to the deceleration analyzer 40 are (1) the times forthe start and end of deceleration, T_(Ds) and T_(De), respectively,obtained from the HR transient detector 30, (2) the areas calculatedfrom the TFR curve by the area calculation module 26, i.e., A_(LF),A_(MF), A_(HF), A_(MH), A_(R), A_(B) and A_(T), and (3) the HR timeseries. The following variables are calculated from the input data:minimum HR during deceleration HR_(D); lag from peak contraction tominimum HR, L_(CD); velocity of decline for HR, V_(D); velocity ofrecovery for HR, V_(R); magnitude of primary acceleration HR_(p); andmagnitude of secondary acceleration HR_(S). The definition of thesevariables is illustrated in FIG. 11 which shows time plots of the HRtime series and the contraction signal.

[0097] In addition, changes in the spectral indices during thedeceleration are also calculated. These will be calculated as changesfrom baseline and will be calculated for two time points during thedeceleration, vis., at the nadir and during recovery. These will bedesignated as A_(Xn) and A_(Xr), respectively, where X ε {LF, MF, HF,MH, R, B, T}.

[0098] Changes in HRV can occur without either substantial or sustainedchanges in HR. Therefore, it is important to also detect transients inHRV. In the monitor of the invention, the variables output from the areacalculation module 26 are examined for transients in the spectraltransient detector module 42. A change in a variable will be consideredsignificant if it exceeds a threshold, δ_(X), and is maintained for aminimum period of time, τ_(X). The following variables will becalculated and output by the module 42: time of transient onset T_(Xo);duration of transient Δ_(XD); time of peak change, T_(Xp); peakmagnitude, A_(Xm); where X is an element of {LF, MF, HF, MH, R, T}.

[0099] Fetal breathing movements are episodic. During a period of fetalbreathing movements, the frequency of breathing effort is usuallybetween 0.7 and 1.1 Hz. Thus, changes in HR associated with therespiratory efforts will have a component in that frequency range. Thiswill be manifested by a preferential increase in A_(HF) and, therefore,an increase in A_(B)=A_(HF)/A_(MF). As described above, an example ofbreathing modulated changes in HRV are shown in FIGS. 2A-2H.

[0100] The inputs to the breathing analyzer 44 are the outputs of thespectral transient detector module 42. A breathing episode will bedefined by preferential increases in A_(HF) and A_(B)=A_(HF)/A_(MF)above thresholds δ_(HF) and δ_(B), respectively, having a minimumduration τ_(HF) and τ_(B), respectively. The output of this module 44 isthe time of the start and end of the breathing episode, T_(Rs) andT_(Re), respectively.

[0101] The inputs to the baseline analyzer module 46 include outputs ofthe HR transient detector 30 and spectral transient detector 42 modulesas well as the calculated areas and the HR time series. These inputs areused to identify periods without transients either in HR or in spectralvariables. Updated baseline values for HR and spectral variables will beaveraged over a window, where the minimum window length is 30 seconds.Outputs from the module 46 include: median HR, HR_(Mb); inner quartilerange, HR_(Qb); and the spectral indices averaged over the window,A_(Xb), where X={LF, MF, HF, MH, R, T}.

[0102] All of the data outputs from the various modules within thefeature extraction subsystem 14 are received by a feature vectorgeneration module 48. The module 48 compiles the data and generates afeature vector or matrix which contains all of the data items used bythe monitor 10 to perform the fetal assessment process. The featurevector is updated each time the HR time series is sampled to generate anew TFR and is output from the feature extraction subsystem 14 as shownat 49.

[0103] A block diagram of the expert subsystem 16 is shown in FIG. 12.The principal function of the expert subsystem 16 is to make anassessment of fetal well-being based on the features being presented toit in the feature vector by the feature extraction subsystem 14 as wellas user input.

[0104] The expert subsystem 16 is a “hybrid” expert system, i.e., itcontains both standard rule-based modules as well as neural networks.Previous attempts at designing expert systems for the evaluation offetal well-being have been exclusively rule-based. However, there areinherent limitations in such an approach. First, it requires that the“rules” used by the “expert” or the clinician be accurately andcompletely extracted and then implemented. Second, an expert must beidentified. Evidence suggests that it is not possible to accomplishthese two goals since the variability between experts in assessing thesame fetal tracings is quite high and the variability in the assessmentof the same fetal trace by a single expert at different times issurprisingly high. In addition, the modification and maintenance of asolely rule-based expert system is labor intensive.

[0105] An alternative approach is to take all the relevant variables andapply them as inputs to a neural network and train the network torecognize patterns associated with good outcomes and bad outcomes. Thefundamental limitation of such an approach is that with currenttechnology it is not possible to determine from the network how thenetwork arrived at its conclusion. This would not be accepted by theclinician.

[0106] The approach taken in the current invention is to break down theanalysis of the fetal tracings into a number of tasks that followsclassical clinical paradigms. Each task may be accomplished using arule-based system or a neural network. In the current configuration,prediction of fetal outcome and the final decision as to overall fetalwell-being are performed by one of a series of neural networks.

[0107] Proper assessment of overall fetal well-being by analysis of HRand HRV requires that certain confounding issues be accounted for. Fourvariables, state, deceleration pattern, acceleration pattern andcontraction pattern, must be assessed from the feature vector, whileother variables such as gestational age must be input by the user. Theexpert subsystem 16 includes four first-stage classifiers which analyzethe feature vector for the four variables.

[0108] The state classifier 50 is a rule-based classifier which assignsa behavioral state to the fetus. The convention of defining behavioralstate will be taken from “Are There Behavioral States in the HumanFetus?” by J. G. Nijhuis et al. in Early Human Development, Volume 6,pp. 177-195, 1982, which is incorporated herein by reference. Thus,possible classification states include 1F, 2F, 3F, 4F, andindeterminate. Briefly, the corresponding heart rate patterns associatedwith each state will be described. State OF is characterized by a stableheart rate with relatively low variability. There can be isolatedaccelerations associated with fetal movements. In state 2F, there is amuch increased HRV with frequent accelerations. In state 3F, HRV isincreased compared to 1F, but, unlike 2F, there are no accelerations. Instate 4F, HR is unstable with large, prolonged accelerations whichcommonly are fused into a sustained tachycardia. States 1F and 2F arethe most commonly observed.

[0109] Updating of state classification will occur every T_(S) seconds.In one embodiment, a default value for T_(S) is 60 seconds, but that canbe changed by user input. If no periodic decelerations have beendetected by the feature extraction subsystem 14, then determination ofstate will be made from time domain and spectral indices of HRV from thelast T_(S) seconds. In the case of decelerations, time domain andspectral indices obtained from the baseline analyzer 46 will be used.Initially, HRV indices will be compared to population statistics forclassification of state. If however, a new state has not been identifiedafter a period T_(D), the last T_(D) minutes of data will be examinedfor relative changes. In one embodiment, a default value for T_(D) is 30minutes, but that can be changed by user input. One possiblecircumstance that must be taken into account is that, in a depressedfetus, a change from a low variability state to a relatively highvariability state may occur, but the HRV during the high state may belower than the value obtained from a population of healthy fetuses. Itis important that the change in state be noted, since a fetus which ischanging states is healthier than one who is not.

[0110] The purpose of the deceleration pattern classifier 52 is to makean initial classification of the overall deceleration pattern as beingearly, late, variable, late/variable or prolonged. The classifier 52will analyze data over a window of a present number L_(C) ofcontractions. In one embodiment, a default number of contractions L_(C)is five. The user can change this number as desired. Update inclassification can occur with each new contraction. The classifier 52will use the following data from the feature vector to make itsclassification: L_(CD), T_(Ts), T_(Te), V_(D), V_(R), HR_(P), HR_(S) andHR_(D). This classifier is preferably rule based but could be a neuralnetwork.

[0111] The purpose of the acceleration pattern classifier 54 is to makean initial classification of the acceleration pattern as beingnonperiodic, periodic or prolonged. The classifier 54 will analyze dataover a window of L_(C) contractions. Update in classification can occurwith each new contraction. The classifier 54 will use the data HR_(A),T_(Ts), and T_(Te) from the feature vector to make its classification.This classifier is preferably rule based but could be a neural network.

[0112] The purpose of the contraction pattern classifier 56 is toanalyze uterine activity. This classifier 56 will only be active if acontraction signal is present. The details of the operation of theclassifier will depend on whether an internal or external uterinemonitor is being used. The classifier will use the following data fromthe feature vector: T_(Cp), C_(P), T_(Cs), T_(Ce), and C_(B). Theclassifier will use data from the past L_(p) contractions and update itsclassification with each new contraction. In one embodiment, the defaultnumber of contractions L_(p) is set at seven. However, this number canbe varied by the user. The contraction pattern will be classified asnormal, increased activity, decreased activity or discoordinateactivity. This classifier is preferably rule based but could be a neuralnetwork.

[0113] To summarize the operation of the four initial classifiers 50,52, 54, 56, each will extract variables from the feature vectorappropriate for its classification task, use rules to determine itsoutput, and output a single variable. The possible values of the outputof each classifier is summarized as follows:

[0114] (1) state classifier: 1F, 2F, 3F, 4F, indeterminate;

[0115] (2) deceleration pattern classifier: early, late, variable,late/variable, prolonged, none;

[0116] (3) acceleration pattern classifier: nonperiodic, periodic,prolonged, none;

[0117] (4) contraction pattern classifier: normal, increased activity,decreased activity, discoordinate activity.

[0118] The classification of the HR and HRV patterns into either healthyor ominous must take into account the context of the situation. Forexample, it is important to know the gestational age of the fetus, thepresent state of the fetus, and the state of labor and other variables.

[0119] The final classification of the HR and HRV pattern will beaccomplished by one of a series of available classifiers 62 in theoutcome predictor module 60. The purpose of the rule-based front end 58is to determine based on inputs from the first-stage classifiers 50, 52,54, 56 and data entered by the user which of the available classifiers62 will analyze the fetal pattern. The rule-based front end will alsodetermine which inputs are extracted from the feature vector andpresented to the selected neural network. As an example, if the outputsof the first four classifiers were 1F (state classifier), variable(deceleration pattern classifier), none (acceleration patternclassifier), and normal (contraction pattern classifier), a specificneural network based classifier 62 would be selected, and theappropriate data would be extracted from the feature vector. If,however, the outputs of the first four classifiers were 1F, none, none,normal, a different neural network classifier 62 would be selected topredict fetal outcome, and a different set of variables would beextracted from the feature vector as input to the different network.

[0120] The outcome predictor module 60 consists of a series of Nclassifiers 62, each of which can preferably include a neural network.The classifier 62 which performs the outcome prediction is selected bythe rule-based front end module 58. Inputs to the outcome predictor 60include data from the feature vector, user input, and outputs from thefirst-stage classifiers 50, 52, 54, 56. The selected classifier 62 willuse data from the previous N_(p) minutes and will update itsclassification every N_(u) minutes. In one embodiment, default valuesfor the time periods N_(p) and Nu are 30 minutes and 10 minutes,respectively. Both of these values can be varied by the user.

[0121] The output of the outcome predictor 60 classifies the overallfetal well-being as either normal, stressed, indeterminate or ominous.The output classification also includes an associated probability.

[0122] Each classifier 62 includes two parts, a preprocessor and neuralnetwork. The preprocessor extracts the correct data from memory andnormalizes each variable. The purpose of the latter function is toensure that each input to a neural network has a value between −1 and 1.Once the data has been extracted and normalized, the neural network isinvoked and the classification of fetal well-being is then made.

[0123] The precise structure of each neural network will vary fromclassifier to classifier. The aspects of the neural networks common toall classifiers will now be described. In one embodiment, the basicstructure of the neural network is a three-layer feed-forward networkwith full interconnections trained using back propagation techniques.The first layer is an input layer, the second layer is a hidden layer,and the third layer is an output layer. Each neuron in the input layerreceives a single input. Each neuron in the hidden layer receives aninput from each of the input neurons. Each connection is weighted, i.e.,the output of neuron I that is connected to hidden neuron j ismultiplied by a weight value W_(ij). Similarly, the interconnectionbetween a hidden neuron j and output neuron o is also weighted byW_(jo). In the preferred embodiment, there are three output neurons: (1)normal, (2) stressed, (3) ominous. Once the specific outcome classifier62 has been selected by the rule-based front end 58 and the appropriatevariable set extracted from the feature vector, the variable set isapplied to the input layer of the classifier 62. Using weights derivedduring training, the values at the output neurons are then predicted. Todetermine the classification, the output neuron with the largestpredicted value is selected. If this value is greater than a threshold,currently selected to be 0.6, then this outcome is selected. If thegreatest magnitude is less than this threshold, then the outcome isclassified as indeterminate. For example, if the output neurons had thevalues normal 0.8, stressed 0.1 and ominous 0.1, the outcome would beclassified as normal. If the outputs were normal 0.5, stressed 0.3,ominous 0.2, the outcome prediction would be classified asindeterminate.

[0124] Training techniques other than back propagation may also be used.Similarly, other structures, such as networks with feed-backconnections, may be used. It will be understood that the type of networkused is not critical to the invention. The details of neural networkswhich can be used to carry out the invention are described in “AnIntroduction to Computing with Neural Nets,” by R. P. Lippmann, IEEEASSP Magazine, pp. 4-22, April, 1987, and “Progress in Supervised NeuralNetworks,” by D. R. Hush and B. G. Horne, IEEE Signal ProcessingMagazine, pp. 8-39, 1993, which are incorporated herein by reference.

[0125] Each of the classifiers 62 can be either an antepartum classifieror an active labor classifier. There are three types of antepartumclassifiers. The type used depends on the signals available. Forantepartum monitoring, a contraction signal is usually available.However, to make the monitor function in as many environments aspractical, an antepartum mode is available that analyzes HR alone. Asecond classifier assumes that HR and contraction signals are available,while a third assumes that HR, contraction and fetal movement signalsare all available. It is unlikely that a contraction signal will not beavailable when a fetal movement signal is, although this possibility isincluded in an alternative realization of the invention. Each neuralnetwork will have as its input time and spectral domain variablesrelated to variability, HR_(Q), HR_(M), and state, as well as theoutputs of the first-stage classifiers 50, 52, 54, 56. Variables relatedto fetal movements and contractions will be used in the appropriateclassifier, if available.

[0126] An important confounding variable is gestational age of thefetus. The gestational age is input by the user. If a gestational agehas not yet been entered, a default value will be used. The effects ofgestational age can be accounted for in one of two ways. The method usedin the monitor of the invention is that gestational age is broken downinto 5 different ranges: <27 weeks, 27-30 weeks, 31-34 weeks, 35-38weeks and >39 weeks. Gestational age is used as an input to the neuralnetworks, with each of the ranges being assigned a constant value.Alternatively, there could be a different classifier 62 for eachgestational age. The division of gestational age into the five divisionsnoted above is based on the present best understanding of fetaldevelopment and are presented only as an example. The ages assigned toeach division as well as the number of divisions can be changed inalternative realizations and may be altered by the user.

[0127] Active labor classifiers assess fetal well-being during activelabor. In order to do so in as accurate a manner as possible, in oneembodiment it is assumed that a contraction signal is available. A fetalmovement signal is considered optional. The design of these classifiersfollows classical obstetric analysis. Therefore there is a uniqueclassifier for each of the following conditions: (1) accelerations, (2)early decelerations, (3) late decelerations, (4) variable decelerations,(5) late/variable decelerations, (6) prolonged decelerations, (7)sinusoidal pattern, and (8) no transients. Gestational age isincorporated into the network as discussed above. Sleep state isincorporated into the network in a manner similar to that forgestational age. Each network has as inputs a unique subset from thefeature vector.

[0128] The output of the neural network outcome predictor 62 selected bythe rule-based front end 58 to evaluate fetal well-being will be eithernormal, stressed, ominous or indeterminate. The value of the predictedoutput will be taken as an estimate of the probability that the patternbelongs to that category. For example, if the outcome classifierpredicts that the pattern is ominous and the value of the associatedoutput neuron is 0.8, then the probability that the pattern belongs tothe ominous category is 0.8.

[0129] The purpose of the rule-based back end module 64 is to organizeinformation from previous classifiers and user input and to format theinformation for display to the user as well as for storage to a diskfile. Information from both the expert subsystem 16 and the featureextraction subsystem 14 is selected for display by the rule-based frontend module 58 and is formatted by the rule-based back end module 64. Thedisplay includes information concerning the predicted outcome, as wellas variables describing the heart rate, heart rate variability, state,contraction pattern, etc.

[0130] The display and storage module 18 organizes data for display andstorage in a data file. There are a number of predefined displayconfigurations which are context sensitive, i.e., the configuration ofthe data on the display depends on the output of the expert subsystem 16and the data selected by the rule-based front end module 58. Inaddition, the user has the ability to define a custom displayconfiguration.

[0131] The goal of the monitor is to present sufficient information tothe user in as simple a format as possible to facilitate assessment offetal status. No clinician is likely to accept the classification madeby the expert subsystem 16 without review of the data. The data used bya clinician includes data from the response to a contraction as well asthe trend variables over time. To be clinically useful, such data arepresented on the display in a clear and concise manner. The literaturehas described what the important variables are for the evaluation of aspecific pattern, e.g., variable decelerations. The important variableschange from one pattern to another, e.g., later decelerations versusaccelerations. The invention automatically presents trend plots of theappropriate data based on the output of the classifiers. However, thiscan be changed by user input.

[0132]FIG. 13 is a schematic illustration of a sample display screen 80on the monitor 10. The display is divided into three windows. The largewindow 82 acts as a type of strip chart in that it displays importantdata as a function of time. In the default configuration for activelabor, the following signals would be displayed from top to bottom: theHR time series, the contraction signal, A_(LF), A_(MF), A_(HF), andA_(T). This provides the clinician with a real-time display of HR,uterine activity and components of HRV. For example, this allows theclinician to view the changes in HRV during a deceleration, importantinformation to the accurate assessment of fetal well-being. In defaultmode, the display graphs the last 20 minutes of data. The user canchange this value to view other segments of data.

[0133] The system 10 stores templates for this window. A template is aparticular configuration, of which the illustrated default is one. Thereare other predefined templates available which might display othercombinations of variables in this window. For example, a differentdefault template could automatically be invoked if a fetal movementsignal becomes available. In this case there would be an additionaltrace of the occurrence of fetal movements. In addition, the user coulddefine and store his/her own template.

[0134] The second window 84 is referred to as a “smart” window. Theinformation displayed in this window is determined by the output of theclassifiers in the expert subsystem 16. For example, where thedeceleration classifier 52 determined that the decelerations werevariable, the display controller would display, using a defaulttemplate, trends of variables that would aid the clinician in assessingthe severity of the decelerations. Thus, the window 84 displays astrends the following data from the last 8 contractions: the minimumheart rate during a deceleration, the velocity of the deceleration, thevelocity of recovery, and A_(LF). The user can choose to examine othervariables in this window 84 such as the magnitude of the decelerationand the lag from peak contraction to the nadir of the deceleration, canchoose from other default templates or can create a template of his/herown.

[0135] The third window 86 is a text window which conveys informationfrom the output of the classifiers, some summary data, the prediction offetal well-being, and recommendations/warnings. In the example shown,information is displayed concerning the state, contraction pattern, anddeceleration pattern.

[0136] In the particular example shown in FIG. 13, the outputs of theinitial four classifiers are presented, i.e., state 2F with duration 7minutes, periodic decelerations of type late and a normal contractionpattern. Some statistical data concerning HRV is also presented. Ananalysis section consists of the output of the outcome classifier withthe associated probability, i.e., normal and p=0.75 for this example.The text window 86 has two additional components labeled warnings andrecommendations in the figure. For this example, there are norecommendations or warnings.

[0137] For stressed or ominous classification, there will be warningsand recommendations. These will follow standard obstetric practice. Forexample, if a classification of ominous was made due to prolongedreduced variability, the following or a similar warning would bedisplayed: “prolonged reduced variability, user input indicates nonarcotics have been used.” This warning reminds the user that narcoticscan substantially reduce HRV without causing fetal compromise. Ifnarcotics have been administered, the user can now enter thisinformation via the keyboard, and a new classification can be performedby the system. For this example, the following or a similarrecommendation would then be displayed: “prolonged low HRV consistentwith depressed fetus, if cause unknown check oxygenation status withscalp blood sample.”

[0138] In an alternative embodiment, the invention provides output datato the clinician in the form of a paper strip chart with multiple datatraces. In this embodiment, the variables which are displayed on thefirst window 82 in the previous embodiment are printed in real time on apaper strip chart. This provides the clinician with a hard copy recordof past readings. As with the screen display, the user can select whichvariables are printed, or can rely on default templates.

[0139] As mentioned above, the monitoring system of the invention can beimplemented in one of many different configurations. A schematic blockdiagram of one preferred realization is shown in FIG. 14. Thisrealization of the invention is implemented on a personal computer (PC)100, although other platforms such as the Macintosh, PowerPC, or a Unixsystem such as the SUN SPARC machines are also possible. The requiredinput to the system is the HR time series generated by an external fetalmonitor 102. Optional inputs include the contraction signal (CON) andthe fetal movement (FM) signal. Typically, all signals come from asingle external fetal monitor 102, although multiple monitors could beused.

[0140] The computer 100 includes a central processing unit (CPU) 104,memory 106, a display monitor 108, storage devices 110, controllers 112,114 to drive the display and storage devices, an analog-to-digitalconverter (ADC) 116, a digital signal processing board (DSP) 118, and aneural network board 120. The ADC 116, DSP 118, and neural network board120 are commercially available products. There is also an optionaloutput board (not shown) for connection to a computer network and/orcentral monitoring station. Additional standard equipment can include akeyboard 122 and a mouse 124 or other suitable pointing device forproviding user inputs of both data and control commands needed toexecute the software which implements the various functions of theinvention.

[0141] The ADC board 116 converts the analog signal from the output ofthe fetal monitor 102 to digital words that can be manipulated by thecomputer 100. In an alternative implementation, the output of the fetalmonitor 102 could be connected to the computer 100 via digital outputs,e.g., a serial RS232 port. The particular implementation is determinedby the output features of the particular fetal monitor. The CPU 104executes the software which makes the computations, controls the ADC116, DSP 118 and neural network boards 120, and controls output to thedisplay 108 and storage device 110 and network communication.

[0142] The purpose of the DSP board 118 is to calculate the non-lineartime-frequency representation (TFR) transformation, thereby removingthis computational burden from the main CPU 104. Thus, the input to theDSP board 118 is the high-pass filtered, analytic HR time series. TheDSP board 118 returns to the CPU 104 the latest estimate of the TFR. Thepurpose of the neural network board 120 is to implement the neuralnetwork outcome predictors 62. The need for separate DSP 118 and neuralnetwork boards 120 is determined by the computational power of the mainCPU 104. With recent increases in microprocessor speeds, it may not benecessary to have separate boards, since some or all of these functionscould be handled by the CPU 104. The need for the separate boards isalso determined by the precise platform on which the invention isimplemented, e.g., SUN workstations being faster than PCS.

[0143] In an alternative realization which is also computer based, a HRtime series of sufficient accuracy may not be available from the fetalmonitor. In this case, the fetal ECG signal is acquired by the computervia either the ADC board 116 or over digital communication lines. Inthis realization, another software module is required to perform R-waverecognition and construction of the HR time series. These latterfunctions can also be implemented on a DSP board, possibly separate fromthe DSP board which calculates the TFR, within the computer.

[0144] A schematic block diagram of another realization of the inventionis shown in FIG. 15. In this realization, the calculation of thetime-frequency representation is incorporated into a stand-alone fetalmonitor 150, while the classification and prediction tasks areimplemented on a digital computer or central monitoring unit 152. Thisconfiguration relieves the digital computer 152 of substantialcomputation load. Consequently, the computer 152 can also function as acentral monitoring station, in which it performs classifications andpredictions of multiple patients, each monitored by a fetal monitor 150.By calculating the TFR within the fetal monitor 150, signals related toHRV, i.e., the areas under the TFR, can be output to a strip chart inaddition to the usual HR and contraction signals. This provides theclinician with much needed quantitative information concerning HRV.Also, since the classifications and predictions are generated at theremote central monitoring unit 152, they are not displayed where thepatient can view them and become upset, possibly needlessly.

[0145] One embodiment of this realization implements the calculation ofthe TFR in hardware. This hardware is incorporated into a fetal monitorwhere the heart rate signal is available from commercially availablecomponents. A schematic block diagram of this portion of the realizationis shown in FIG. 16. The inputs are the usual signals for determining HR(fetal ECG and ultrasound) and the contraction signal. The block labeledPrior Art Components 156 contains the processors which extract the HRtime series from the input signals. This HR time series signal is thenoutput to the components of the invention beginning with the high-passfilter 20. The filtered HR times series is then forwarded to the HilbertTransformer 22 which makes the signal analytic. The analytic filteredsignal is then passed to the processor 24 which calculates the TFR. Theamplitude spectrum from the TFR calculation is passed to the nextprocessor 26 which calculates the areas under the curve over predefinedfrequency bands. The output of the area calculation processor 26 is thenavailable for display on the strip chart 154.

[0146] The HR signal is output to the strip chart as are the contractionand FM signals if they are available. One or more of the areas (or sumand/or ratios of the various areas) are also output to the strip chart154. There is a default setting which includes total area A_(T). Othersignal(s) could be selected by user input. By displaying A_(T), alongwith HR and contraction signals, the system enables the clinician tobetter evaluate HRV and, thus, fetal well-being. All variables,including HR, CON, FM, and the areas, are available for output to thecentral monitoring station 152 via digital communication lines 160.

[0147] The central monitoring station 152 is a computer-based systemwhich performs the functions of the feature extraction subsystem 14,expert subsystem 16, and display and storage module 18 shown in FIG. 8.Additional software modules are included to allow monitoring of a numberof remote fetal monitors 150 as well as classification of more than onefetus.

[0148] In one preferred embodiment of the invention, the user has theoption of reviewing the raw TFR data at any time or can substitute athree-dimensional display of the TFR for one of the three displaywindows described above. The user can also optionally fill the entiredisplay with the three-dimensional TFR display. An example of thethree-dimensional TFR plot is shown in FIG. 17. The example of FIG. 17shows a smoothed Wigner spectrum in the form offrequency-versus-magnitude calculated on fetal heart rate over time. Theplot represents the spectral content of the heart rate signal over afrequency range of 0 to 1 Hz for five minutes of data where the spectrawere calculated at 1.0-second intervals. Such a plot is rich in detailregarding fetal heart rate variability. First, an overall indication ofHRV is obtained. A moribund fetus would have a relatively flat spectrumover the observation time. In addition, a plot over a longer period,e.g., 20 minutes, would enable the clinician to clearly observe a changein sleep state, since such changes are associated with well documentedchanges in HRV. This is particularly important in cases where HRV is lowwhere it is critical to determine whether the fetus is sleeping or indistress.

[0149] A plot such as the one in FIG. 17 also provides the clinicianwith a great deal of information concerning transient changes in fetalHRV. Such transient changes are important in interpreting decelerationsduring labor. Increases in variability during a deceleration indicatethe presence of intact compensatory mechanisms in the fetus and cantherefore be reassuring. However, decreased variability during adeceleration is considered an ominous sign. Transient changes observedover a certain range of frequencies will also aid in fetal assessment.As an example, the transient increase in the Wigner distribution shownin FIG. 17 in the frequency range 0.5 to 1.0 Hz at approximately 225seconds is consistent with respiratory modulated changes in HRV. Suchindices of fetal breathing are important in fetal assessment. Finally,the appearance of a sinusoidal pattern, which has important diagnosticimplications, would be easily identified from such a plot as welldefined sharp peak in the spectra.

[0150] The three-dimensional TFR plot of FIG. 17 can also be displayedalongside a plot of other timing signals such as the uterine contractionsignal or the fetal movement signal. The clinician can use the visualdisplay to quickly ascertain the relationships among the varioussignals. For example, the clinician can readily identify late and/orearly decelerations in HRV with respect to uterine contractions as partof the process of determining overall fetal well-being. Alternatively,the TFR plot can be color coded to indicate uterine contractions such asby plotting the TFR in one color during a contraction and in anothercolor between contractions.

[0151] While this invention has been particularly shown and describedwith references to preferred embodiments thereof, it will be understoodby those skilled in the art that various changes in form and details maybe made therein without departing from the spirit and scope of theinvention as defined by the appended claims.

1. A fetal data processing method comprising: providing a fetal heartrate moniter; receiving a signal indicative of fetal heart rate timedata for the fetus from the monitor, said fetal heart rate time dataindicating heart rate of the fetus over time; performing a non-lineartime-frequency transformation on the fetal heart rate time data togenerate a frequency domain representation for the fetal heart rate timedata; and analyzing the frequency domain representation to indicate acondition of the fetus related to the fetal heart rate time data.
 2. Themethod of claim 1 further comprising indicating transient change infetal heart rate variability.
 3. The method of claim 1 furthercomprising indicating transient change in fetal heart rate.
 4. Themethod of claim 1 wherein the fetal heart rate time data is also used toperform the indicating step.
 5. The method of claim 1 wherein a uterinecontraction signal is also used to perform the indicating step.
 6. Themethod of claim 1 wherein a fetal movement signal is also used toperform the indicating step.
 7. The method of claim 1 further comprisingpredicting outcome for the fetus.
 8. The method of claim 1 wherein theindicating step comprises applying a set of classification rules tofetal data.
 9. The method of claim 1 wherein the indicating stepcomprises applying a pattern of fetal data to a neural network.
 10. Themethod of claim 1 wherein the indicating step comprises analyzing thetime-frequency representation in plural frequency bands.
 11. The methodof claim 1 wherein the time-frequency representation spans a frequencyrange from 0.02 Hz to 1.1 Hz.
 12. The method of claim 1 wherein theindicating step comprises computing areas under the time-frequencyrepresentation.
 13. The method of claim 1 wherein the time-frequencyrepresentation is generated for fetal heart rate time data from a timeperiod of less than ten seconds.
 14. The method of claim 1 furthercomprising sampling the fetal heart rate time data at a frequencygreater than 2 Hz.
 15. The method of claim 1 further comprisingdisplaying in real time variables used to indicate the condition of thefetus.
 16. The method of claim 1 further comprising displaying athree-dimensional plot of the time-frequency representation.
 17. Themethod of claim 16 , wherein the three-dimensional plot of thetime-frequency representation is updated with frequency amplitude dataat 1.0 second intervals.
 18. The method of claim 16 , wherein thethree-dimensional plot of the time-frequency representation is colorcoded to indicate different conditions.
 19. The method of claim 1further comprising generating recommendations regarding fetalwell-being.
 20. The method of claim 1 wherein the non-lineartime-frequency representation is performed for each sampling period overwhich fetal heart rate time date is collected where the sampling periodis not to exceed 10 seconds.
 21. The method of claim 1 wherein thetime-frequency representation is generated using a smoothed Wignerdistribution.
 22. A fetal data processing system comprising: a fetalheart rate monitor; an input that receives fetal heart rate time datafor a fetus from the monitor, said fetal heart rate time data indicatingheart rate of the fetus over time; and a processing system comprising aprocessor and a memory, the processor running under control of a programstored in the memory that receives the fetal heart rate time data, andfurther comprising a subsystem that samples the fetal heart rate timedata over a sample time period that does not exceed 10 seconds such thatsampled fetal heart time data is generated, the system performing atime-frequency transformation on the sampled fetal heart rate time data,said transformation generating a frequency-domain representation for thefetal heart rate time data.
 23. The fetal data processing system ofclaim 22 further comprising a classifying subsystem that analyzes thefetal heart rate time data to indicate the condition of the fetus. 24.The fetal data processing system of claim 23 wherein the classifyingsubsystem also analyzes a uterine contraction signal to indicate thecondition of the fetus.
 25. The fetal data processing system of claim 23wherein the classifying subsystem also analyzes a fetal movement signalto indicate the condition of the fetus.
 26. The fetal data processingsystem of claim 23 wherein the classifying subsystem analyzes thetime-frequency representation in plural frequency bands.
 27. The fetaldata processing system of claim 23 wherein the classifying subsystemapplies a set of classifying rules to a pattern of fetal data toclassify the fetal data.
 28. The fetal data processing system of claim23 wherein the classifying subsystem comprises at least one neuralnetwork for receiving a pattern of fetal data to classify the fetaldata.
 29. The fetal data processing system of claim 23 wherein theclassifying subsystem predicts an outcome for the fetus.
 30. The fetaldata processing system of claim 22 further comprising a display fordisplaying in real time variables used to indicate the condition of thefetus.
 31. The fetal data processing system of claim 22 furthercomprising a display controller for selecting for display in real timevariables used to predict outcome for the fetus.
 32. The fetal dataprocessing system of claim 22 wherein the frequency-domainrepresentation is analyzed to indicate, based on the analysis of thefrequency-domain representation, a condition of the fetus related to thesampled fetal heart rate time data.